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Merge pull request #953 from DnlRKorn/Issue952
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Update data-integration.md to fix Figure numbering discrepancies detailed in Issue 952.
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matentzn authored Jul 8, 2024
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Expand Up @@ -182,9 +182,9 @@ Note though that such methods lack the transparency of basic methods, which mean

![uPheno integration](../images/phenotype-integration.png)

!!! note "Figure: data integration with uPheno"
!!! note "Figure 3: data integration with uPheno"

The Figure shows 4 different kinds of integration:
Figure 3 shows 4 different kinds of integration:

- A: Measurement data. A measurement in conjunction with a normal range and a mapping to a trait term is transformed to a Phenotypic abnormality term in HPO.
- B: Unstructured data. Free text, for example in a paper, is translated into pre-coordinated uPheno expressions
Expand All @@ -210,11 +210,11 @@ In the following we discuss a few of the most common forms of knowledge.
_Core ontological relationships_ such as "is-a" or "part-of" are the most boring of all kinds of knowledge, but they have a huge potential for data analysis.
For example, in Figure 1 above we can see that "Hypolysinemia" (a human phenotype) is a subclass of "decreased level of lysine in the blood" (a species independent class).

This is already nice, but lets look at what we _really_ get when we employ uPheno in Figure 2:
This is already nice, but lets look at what we _really_ get when we employ uPheno in Figure 4:

![uPheno Class Hierarchy](../images/upheno_hierarchy.png)

!!! note "Figure 2: uPheno class hierarchy of Hypolysinemia."
!!! note "Figure 4: uPheno class hierarchy of Hypolysinemia."

The class hierarchy of uPheno, rendered using OLS. The screenshot only
displays a fraction of the actual hierarchy, which is heavily poly-hierarchical.
Expand All @@ -230,7 +230,7 @@ Here we can see just how deeply a concept like "Hypolysinemia" can be integrated

!!! warning

The exact naming conventions in uPheno are under review at the moment, so the reader may experience some discrepancies between Figure 2, the listing above, and the [ontology in Monarch's OLS](https://ols.monarchinitiative.org/ontologies/upheno2).
The exact naming conventions in uPheno are under review at the moment, so the reader may experience some discrepancies between Figure 4, the listing above, and the [ontology in Monarch's OLS](https://ols.monarchinitiative.org/ontologies/upheno2).

Not everyone will agree that all of these groupings are particularly useful (`changed blood amino acid level` may not have that many realy world use cases),
but the fact that we _can_ aggregate our data on so many levels is compelling.
Expand Down Expand Up @@ -346,9 +346,9 @@ All of these phenotype assocations can be augmented with many others, such as ge

![uPheno Integration of Knowledge](../images/integration_links.png)

!!! note "Figure: Integrating Knowledge in the uPheno framework."
!!! note "Figure 5: Integrating Knowledge in the uPheno framework."

This picture looks complicated, but it shows only a fraction of the available relationships.
Figure 5 looks complicated, but it shows only a fraction of the available relationships.
Most of the relationships are phenotypic or core ontological, only the Hypolysinemia link to `SLC7A7` is an KG associations.
There are dozens of different kinds of assocations that could be added here!

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